Agent-based tools for modeling and simulation of self-organization in peer-to-peer, ad hoc, and other complex networks

Niazi, Muaz A.; Hussain, Amir

doi:10.1109/mcom.2009.4804403

Cited by 106 publications

(52 citation statements)

References 7 publications

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“…It consists of nodes and edges joining them. Many complex systems from a variety of domains are represented as network including environmental modeling (Niazi et al 2010), business systems (Aoyama 2002), wireless sensors, and ad-hoc networks (Niazi and Hussain 2009). Networks are rich in information, having a range of local and global properties.…”

Section: Sentiment Analysismentioning

confidence: 99%

Sentiment analysis and the complex natural language

Khan

Durrani

Ali

et al. 2016

Complex Adapt Syst Model

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There is huge amount of content produced online by amateur authors, covering a large variety of topics. Sentiment analysis (SA) extracts and aggregates users’ sentiments towards a target entity. Machine learning (ML) techniques are frequently used as the natural language data is in abundance and has definite patterns. ML techniques adapt to domain specific solution at high accuracy depending upon the feature set used. The lexicon-based techniques, using external dictionary, are independent of data to prevent overfitting but they miss context too in specialized domains. Corpus-based statistical techniques require large data to stabilize. Complex network based techniques are highly resourceful, preserving order, proximity, context and relationships. Recent applications developed incorporate the platform specific structural information i.e. meta-data. New sub-domains are introduced as influence analysis, bias analysis, and data leakage analysis. The nature of data is also evolving where transcribed customer-agent phone conversation are also used for sentiment analysis. This paper reviews sentiment analysis techniques and highlight the need to address natural language processing (NLP) specific open challenges. Without resolving the complex NLP challenges, ML techniques cannot make considerable advancements. The open issues and challenges in the area are discussed, stressing on the need of standard datasets and evaluation methodology. It also emphasized on the need of better language models that could capture context and proximity.

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Section: Sentiment Analysismentioning

confidence: 99%

Sentiment analysis and the complex natural language

Khan

Durrani

Ali

et al. 2016

Complex Adapt Syst Model

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“…Niazi (2013) has reported ABM as a potential candidate for modeling complex adaptive system like wireless sensor networks, swarm robotic networks, self-assembling robots, peer-to-peer networks. Niazi and Hussain (2009) have presented a self-organizable agent-based model for P2P/ad hoc networks and complex systems keeping in mind their interaction with environment/humans/ animals. Niazi and Hussain (2011a) have presented a novel formal agent-based simulation framework (FABS) to improve the sensing capability of wireless sensor networks in a complex adaptive environment.…”

Section: Modeling Vcps As Complex Adaptive Systemmentioning

confidence: 99%

Road collisions avoidance using vehicular cyber-physical systems: a taxonomy and review

Riaz

Niazi

2016

Complex Adapt Syst Model

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Road traffic is known to have its own complex dynamics. One implication of complexity is that road traffic collisions have become an unwelcome but unavoidable part of human life. One of the major causes of collisions is the human factor. While car manufacturers have been focusing on developing feasible solutions for autonomous and semi-autonomous vehicles to replace or assist human drivers, the proposed solutions have been designed only for individual vehicles. The road traffic, however, is an interaction-oriented system including complex flows. Such a system requires a complex systems approach to solving this problem as it involves considering not only pedestrians, road environment, but also road traffic which can include multiple vehicles. Recent research has demonstrated that large-scale autonomous vehicular traffic can be better modeled using a collective approach as proposed in the form of vehicular cyber-physical systems (VCPS) such as given by Li et al. (IEEE Trans Parallel Distrib Syst 23(9):1775–1789, 2012) or Work et al. (Automotive cyber physical systems in the context of human mobility. In: National workshop on high-confidence automotive cyber-physical systems, Troy, MI, 2008). To the best of our knowledge, there is currently no comprehensive review of collision avoidance in the VCPS. In this paper, we present a comprehensive literature review of VCPS from the collision-avoidance perspective. The review includes a careful selection of articles from highly cited sources presented in the form of taxonomy. We also highlight open research problems in this domain.

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“…All of these factors prevent people from getting whole picture of message diffusion when mass incident happens. Considering that agent based simulation can handle the complexity of solutions through decomposing, modeling, and organizing the interrelationships among components [6] and this method has been successfully applied in a large number of works [7], we introduce it into the discussion on mass incident diffusion. Although we only focus our research on mass incident diffusion in Weibo (Chinese Twitter) since it is one of the most popular social network platforms in China, we do believe that the analysis in this paper can be easily extended to the analysis on mass incidents diffusion in other social network platforms by similarity arguments.…”

Section: Introductionmentioning

confidence: 99%

Based on Agent Model and K-Core Decomposition to Analyze the Diffusion of Mass Incident in Microblog

Pan¹,

Shen²,

Zhong³

2017

Complexity

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Mass incidents, which may influence the stability and security of the society in China, are getting more and more attentions not only from policy makers but also from Chinese social researchers. Catching the diffusion mechanism is believed to be critical to understand essential of these mass incidents since message dissemination plays important roles in every stage of mass incident. Recently, online social networks including Weibo (Chinese Twitter) become more and more popular in China. There are reports showing that Weibo discussion has accompanied the processes of most mass incidents happening in China in these few years. So, in this paper, we aim at introducing -Core decomposition method from complex network to the analysis on how to manage the diffusion of mass incident in Weibo based on agent based model which can simulate Weibo user's actions when mass incident happens. This work can help people understand how mass incident messages spread across the network. And then, people may have better strategy to manage the diffusion of mass incidents.

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Agent-based tools for modeling and simulation of self-organization in peer-to-peer, ad hoc, and other complex networks

Cited by 106 publications

References 7 publications

Sentiment analysis and the complex natural language

Sentiment analysis and the complex natural language

Road collisions avoidance using vehicular cyber-physical systems: a taxonomy and review

Based on Agent Model and K-Core Decomposition to Analyze the Diffusion of Mass Incident in Microblog

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